{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 100.     4.     9.3]\n",
      " [  50.     3.     4.8]\n",
      " [ 100.     4.     8.9]\n",
      " [ 100.     2.     6.5]\n",
      " [  50.     2.     4.2]\n",
      " [  80.     2.     6.2]\n",
      " [  75.     3.     7.4]\n",
      " [  65.     4.     6. ]\n",
      " [  90.     3.     7.6]\n",
      " [  90.     2.     6.1]]\n"
     ]
    }
   ],
   "source": [
    "#载入数据\n",
    "data = np.genfromtxt(\"Delivery.csv\",delimiter=\",\")\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 100.    4.]\n",
      " [  50.    3.]\n",
      " [ 100.    4.]\n",
      " [ 100.    2.]\n",
      " [  50.    2.]\n",
      " [  80.    2.]\n",
      " [  75.    3.]\n",
      " [  65.    4.]\n",
      " [  90.    3.]\n",
      " [  90.    2.]]\n",
      "[ 9.3  4.8  8.9  6.5  4.2  6.2  7.4  6.   7.6  6.1]\n"
     ]
    }
   ],
   "source": [
    "x_data = data[:,:-1]\n",
    "y_data = data[:,-1]\n",
    "print(x_data)\n",
    "print(y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model =linear_model.LinearRegression()\n",
    "model.fit(x_data,y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coefficients: [ 0.0611346   0.92342537]\n",
      "intercept: -0.868701466782\n",
      "predict: [ 9.06072908]\n"
     ]
    }
   ],
   "source": [
    "#系数\n",
    "print(\"coefficients:\",model.coef_)\n",
    "#截距\n",
    "print(\"intercept:\",model.intercept_)\n",
    "#测试\n",
    "x_test = [[102,4]]\n",
    "predict = model.predict(x_test)\n",
    "print(\"predict:\",predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Xr9YxTm6w54+tJpZIJJBMJsFxnKktlapbSqgWBUm6QOaW3giwlq2W7sJq1yIO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21baa8ea630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.figure().add_subplot(111,projection = '3d')\n",
    "ax.scatter(x_data[:,0],x_data[:,1],y_data,c = 'r',marker = 'o',s = 100)\n",
    "x0 = x_data[:,0]\n",
    "x1 = x_data[:,1]\n",
    "\n",
    "#生成网格矩阵\n",
    "x0,x1 = np.meshgrid(x0,x1)\n",
    "z = model.intercept_ + model.coef_[0] *x0 + model.coef_[1] *x1\n",
    "\n",
    "#画3D图\n",
    "ax.plot_surface(x0,x1,z)\n",
    "\n",
    "#设置坐标轴\n",
    "ax.set_xlabel('Miles')\n",
    "ax.set_ylabel('Num of Deliveries')\n",
    "ax.set_zlabel('Time')\n",
    "\n",
    "#显示图像\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
